ESSAYS ON DEMAND RESPONSE AND A SUPPLY CHAIN MODEL FOR A VERTICALLY INTEGRATED OIL COMPANY
Open Access
- Author:
- Mina Carbonero, Roger
- Graduate Program:
- Energy and Mineral Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- October 17, 2019
- Committee Members:
- Seth Adam Blumsack, Dissertation Advisor/Co-Advisor
Seth Adam Blumsack, Committee Chair/Co-Chair
Chiara Lo Prete, Committee Member
Zhen Lei, Committee Member
Edward C Jaenicke, Outside Member
Mort D Webster, Program Head/Chair - Keywords:
- Demand Response
Electricity Markets
Critical Peak Events
Dynamic Tariffs
Oil and Gas Supply Chain - Abstract:
- When electricity demand is at or near its highest level, more expensive and less efficient generators have to be dispatched by the system operator in order to meet the additional peak demand. The utilization of these units results in higher generating costs that translates into higher wholesale electricity prices. In some markets within the United States wholesale prices can fluctuate from less than 5 cents per kWh to as much as 30 cents per kWh. Under these circumstances, even a small decrease in electricity demand can result in a significant reduction in wholesale and aggregate end-user prices. In order to achieve these reductions, peak consumption electricity prices can be changed over time in such a way that they reflect wholesale prices more closely. Assuming that electricity customers are price responsive, customers would react to this variation in prices by changing their consumption patterns. This reaction to changes in electricity prices is known as Demand Response (DR). Although several pilot studies have found evidence of customers’ responsiveness to price signals, the reported levels of response to similar changes in price are still variable; making price-based DR programs an unreliable model for dealing with system contingencies and planning. However, as more data becomes available, it will be possible to have a more accurate understanding of the potential for broader implementation of Economic DR programs. Using data from a pilot study carried out by Green Mountain Power (GMP) as a component of the eEnergy Vermont Smart Grid project in Rutland, VT I performed a series of studies that seek to provide evidence of changes in electricity demand due to price signals. The proposed dissertation is organized as follows: Chapter 2 of this thesis provides more details on the origins, legislation and current status of demand response in the United States, including a review of the current literature on dynamic pricing that shows different methodological trends. Additionally, I examine five Consumer Behavior Studies (CBS) funded by the Smart Grid Investment Grant (SGIG). The studies analyze different dynamic tariffs and some of them discuss the importance of enabling technologies. Research shows that customers do respond to dynamic pricing. However, more evidence is needed in order to assess the persistence of these responses. Chapter 3 provides an impact analysis of a residential DR pilot study carried out by Green Mountain Power in the city of Rutland, Vermont. This analysis provides evidence of reductions in electricity demand during critical peak events due to price signals. In Chapter 4 I estimate the relative impact of socioeconomic factors on households’ electricity consumption patterns. I use a Descriptive Factor Analysis (DFA) to group previously measured variables that are used as a predictor of electricity consumption. Results of this analysis indicate that, among the measured predictors, number of air conditioners, ceiling fans and people living in the household, have the biggest influence in determining customer responsiveness to price signals during critical peak events. Chapter 5 is a survival analysis of the GMP pilot program. The obtained evidence suggests that attempting to change customers from the Critical Peak Rebate (CPR) to the Critical Peak Pricing (CPP) treatment group appears to have a dramatic effect on drop-out rates. Additionally, providing households with an in-home display reduces by three times the risk of dropping out when facing the possibility of transitioning from a CPR to a CPP tariff. Also, receiving and in-home display had a significant effect on households enrolled in the CPP group. The obtained hazard rate for the Critical Peak Pricing with in-home display (CPP-I) group is half that of the CPP group. Finally, chapter 6 presents a model for supply chain planning of a vertically integrated oil company. The developed model maximizes the utility of a vertically integrated oil company including multimodal transportation, subsidiaries, and financial metrics. This tool reduces the discontinuity of the operational planning process and allows the vertically integrated oil company to take the best optimization decisions taking into consideration risks associated to potential affections of the oil transport infrastructure.